{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "4151ac7f",
   "metadata": {},
   "outputs": [],
   "source": [
    "import sklearn\n",
    "import numpy as np\n",
    "import os\n",
    "import pandas as pd\n",
    "\n",
    "%matplotlib inline\n",
    "import matplotlib as mpl\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.metrics import roc_auc_score ,f1_score,roc_curve\n",
    "from sklearn.pipeline import make_pipeline\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.svm import SVC\n",
    "\n",
    "import lightgbm as lgb\n",
    "np.random.seed(42)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c5d01570",
   "metadata": {},
   "source": [
    "### Outline\n",
    "\n",
    "1. Data cleaning and EDA\n",
    "2. Building model and tunning\n",
    "3. Result analysis: LightBGM: ~0.82 auc socore\n",
    "\n",
    "Answer these questions:\n",
    "\n",
    "**- What are some notable features of this data set?**\n",
    "\n",
    "1. LightGBM算法中，对节点划分贡献最大的features有：newapp_year, monthlyfixedincome, professionid, avg_sale_apartment_price_10000,legalzipcode/long/lat.\n",
    "\n",
    "2. 一些feature奇怪的值，distance_residence_company 大多数是0\n",
    "\n",
    "\n",
    "**- What are some potential drawbacks of your solution?**\n",
    "\n",
    "1. More advanced method to deal with missing, current method is replace with mean/median. Espically with location value.和坐标有关的数值缺失可以将该地的坐标/邮编对应的一个区域范围内随机分配一个值。\n",
    "2. 神经网络的模型训练过程loss不变。\n",
    "\n",
    "\n",
    "**- How do you think about whether your algorithm might be overfitting?**\n",
    "1. 画出训练过程training loss vs validation loss的图，training Loss在一直下降而valid Loss没有下降甚至上升说明模型过拟合。\n",
    "\n",
    "\n",
    "**- What's the runtime of your algorithm in training and testing? Please provide both:**\n",
    " \n",
    " (a) A theoretical analysis (a rough, hand-wavy analysis would be fine)\n",
    " \n",
    " LR: 梯度下降的每一次迭代需要计算每一个点的梯度，所以时间和和f feature 数量， n 样本数量， Epoch数都有关。\n",
    " ~O(f n E)\n",
    " \n",
    " Ensenble trees: 和每一颗数生长的子叶的深度d有关，一共集成多少颗数n有关, feature数f O(log(d) n f)\n",
    " \n",
    " (b) The amount of time that your computer actually spent on the dataset\n",
    " \n",
    " LR: 10s\n",
    " Ensenble trees: 3分钟左右\n",
    " \n",
    "\n",
    "**- If you had time to work on this problem for another week, what else would you do beyond\n",
    "what you've done here?**\n",
    "\n",
    "1. 尝试更好的缺省值处理方法。特征工程更好的挖掘地理信息相关参数，lat,long, zip code, distanace等等。\n",
    "2. 解决神经网络训练不起来的原因。\n",
    "3. 样本类别不平衡问题。\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "60a2007a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(200000, 62)"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "file_name = './sample_200_0k_20170120.csv'\n",
    "df = pd.read_csv(file_name)\n",
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "5619d848",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 200000 entries, 0 to 199999\n",
      "Data columns (total 62 columns):\n",
      " #   Column                          Non-Null Count   Dtype  \n",
      "---  ------                          --------------   -----  \n",
      " 0   gender                          200000 non-null  object \n",
      " 1   birthdate                       200000 non-null  object \n",
      " 2   maritalstatus                   199992 non-null  object \n",
      " 3   numofdependence                 200000 non-null  int64  \n",
      " 4   education                       200000 non-null  object \n",
      " 5   professionid                    200000 non-null  object \n",
      " 6   homestatus                      200000 non-null  int64  \n",
      " 7   staysinceyear                   200000 non-null  float64\n",
      " 8   EmploymentSinceYear             108860 non-null  float64\n",
      " 9   MainBusinessSinceYear           94683 non-null   float64\n",
      " 10  jobtypeid                       199863 non-null  object \n",
      " 11  jobpos                          114845 non-null  object \n",
      " 12  monthlyfixedincome              200000 non-null  float64\n",
      " 13  monthlyvariableincome           200000 non-null  float64\n",
      " 14  spouseincome                    199992 non-null  float64\n",
      " 15  newapplicationdate              200000 non-null  object \n",
      " 16  MaxOverDueDays                  200000 non-null  float64\n",
      " 17  residencezipcode                200000 non-null  float64\n",
      " 18  companyzipcode                  195819 non-null  float64\n",
      " 19  legalzipcode                    200000 non-null  float64\n",
      " 20  residence_lat                   200000 non-null  float64\n",
      " 21  residence_long                  200000 non-null  float64\n",
      " 22  company_lat                     195819 non-null  float64\n",
      " 23  company_long                    195819 non-null  float64\n",
      " 24  legal_lat                       200000 non-null  float64\n",
      " 25  legal_long                      200000 non-null  float64\n",
      " 26  birthplace                      200000 non-null  object \n",
      " 27  avg_income                      195700 non-null  float64\n",
      " 28  std_income                      195700 non-null  float64\n",
      " 29  avg_income_cnt                  195700 non-null  float64\n",
      " 30  avg_income_nation               199882 non-null  float64\n",
      " 31  std_income_nation               199882 non-null  float64\n",
      " 32  avg_income_nation_cnt           199882 non-null  float64\n",
      " 33  avg_income_area                 195818 non-null  float64\n",
      " 34  std_icnome_area                 195818 non-null  float64\n",
      " 35  avg_income_area_cnt             195818 non-null  float64\n",
      " 36  avg_sale_house_price_5000       200000 non-null  float64\n",
      " 37  std_sale_house_price_5000       200000 non-null  float64\n",
      " 38  sale_house_cnt_5000             200000 non-null  float64\n",
      " 39  avg_sale_apartment_price_5000   200000 non-null  float64\n",
      " 40  std_sale_apartment_price_5000   200000 non-null  float64\n",
      " 41  sale_apartment_cnt_5000         200000 non-null  float64\n",
      " 42  avg_rent_house_price_5000       200000 non-null  float64\n",
      " 43  std_rent_house_price_5000       200000 non-null  float64\n",
      " 44  rent_house_cnt_5000             200000 non-null  float64\n",
      " 45  avg_rent_apartment_price_5000   200000 non-null  float64\n",
      " 46  std_rent_apartment_price_5000   200000 non-null  float64\n",
      " 47  rent_apartment_cnt_5000         200000 non-null  float64\n",
      " 48  avg_sale_house_price_10000      200000 non-null  float64\n",
      " 49  std_sale_house_price_10000      200000 non-null  float64\n",
      " 50  sale_house_cnt_10000            200000 non-null  float64\n",
      " 51  avg_sale_apartment_price_10000  200000 non-null  float64\n",
      " 52  std_sale_apartment_price_10000  200000 non-null  float64\n",
      " 53  sale_apartment_cnt_10000        200000 non-null  float64\n",
      " 54  avg_rent_house_price_10000      200000 non-null  float64\n",
      " 55  std_rent_house_price_10000      200000 non-null  float64\n",
      " 56  rent_house_cnt_10000            200000 non-null  float64\n",
      " 57  avg_rent_apartment_price_10000  200000 non-null  float64\n",
      " 58  std_rent_apartment_price_10000  200000 non-null  float64\n",
      " 59  rent_apartment_cnt_10000        200000 non-null  float64\n",
      " 60  previous                        78978 non-null   object \n",
      " 61  distance_residence_company      195819 non-null  float64\n",
      "dtypes: float64(50), int64(2), object(10)\n",
      "memory usage: 94.6+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "id": "aeaa9b84",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "maritalstatus 8 -- 0.0\n",
      "EmploymentSinceYear 91140 -- 0.4557\n",
      "MainBusinessSinceYear 105317 -- 0.5266\n",
      "jobtypeid 137 -- 0.0007\n",
      "jobpos 85155 -- 0.4258\n",
      "spouseincome 8 -- 0.0\n",
      "companyzipcode 4181 -- 0.0209\n",
      "company_lat 4181 -- 0.0209\n",
      "company_long 4181 -- 0.0209\n",
      "avg_income 4300 -- 0.0215\n",
      "std_income 4300 -- 0.0215\n",
      "avg_income_cnt 4300 -- 0.0215\n",
      "avg_income_nation 118 -- 0.0006\n",
      "std_income_nation 118 -- 0.0006\n",
      "avg_income_nation_cnt 118 -- 0.0006\n",
      "avg_income_area 4182 -- 0.0209\n",
      "std_icnome_area 4182 -- 0.0209\n",
      "avg_income_area_cnt 4182 -- 0.0209\n",
      "previous 121022 -- 0.6051\n",
      "distance_residence_company 4181 -- 0.0209\n"
     ]
    }
   ],
   "source": [
    "#find columns with null value and percentage\n",
    "null_col = []\n",
    "for c in df.columns:\n",
    "    if df[c].isnull().sum() != 0:\n",
    "        print(c, df[c].isnull().sum(), '--',round(df[c].isnull().sum()/df.shape[0],4))\n",
    "        null_col.append(c)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "be516a38",
   "metadata": {},
   "source": [
    "### Feature enginnering\n",
    "1. convert time: birthdate, newapplicationdate\n",
    "2. Encode categ variables: maritalstatus,education,professionid,jobtypeid,jobpos\n",
    "3. create label： MaxOverDueDays>90 as overdue(1) else 0\n",
    "4. drop birthplace feature, to large ans discrete\n",
    "5. drop previous\n",
    "6. how to deal with nan values? \n",
    "    \n",
    "    3 features have over 5% is null \n",
    "    \n",
    "    EmploymentSinceYear -> replace nan with mode/mean/distribution,\n",
    "    \n",
    "    MainBusinessSinceYear -> OR try by experiment\n",
    "    \n",
    "    jobpos -> replace with mod/"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "id": "dffe1dc3",
   "metadata": {},
   "outputs": [],
   "source": [
    "#create label\n",
    "df['y'] = (df.MaxOverDueDays > 90).astype(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "id": "9540a641",
   "metadata": {},
   "outputs": [],
   "source": [
    "from datetime import datetime, timedelta, timezone\n",
    "\n",
    "#convert birthdate to age\n",
    "age_series = pd.to_datetime(df.birthdate).dt.date\n",
    "age_series = (pd.to_datetime('today').normalize().date() - age_series)\n",
    "age_series = age_series.dt.total_seconds() / (365.23*24*3600)\n",
    "df['age'] = age_series\n",
    "\n",
    "app_series = pd.to_datetime(df.newapplicationdate).dt.date\n",
    "df['newapp_date'] = app_series.apply(lambda x: x.day)\n",
    "df['newapp_month'] = app_series.apply(lambda x: x.month)\n",
    "\n",
    "app_series = (pd.to_datetime('today').normalize().date() - app_series)\n",
    "#app_series_date = app_series\n",
    "app_series_year = app_series.dt.total_seconds() / (365.23*24*3600)\n",
    "df['newapp_year'] = app_series_year\n",
    "df['newapp_day_count'] = app_series.dt.total_seconds() / (24*3600)\n",
    "\n",
    "# encode categ var\n",
    "from sklearn import preprocessing\n",
    "from sklearn.preprocessing import OrdinalEncoder\n",
    "LE = preprocessing.LabelEncoder()\n",
    "\n",
    "df['maritalstatus_code'] = LE.fit_transform(df.maritalstatus)\n",
    "df['professionid_code'] = LE.fit_transform(df.professionid)\n",
    "df['birthplace_code'] = LE.fit_transform(df.birthplace)\n",
    "df['gender_code'] = LE.fit_transform(df.gender)\n",
    " \n",
    "# order encode\n",
    "ed_map = {\n",
    "    'SENIOR_HIGH_SCHOOL' :3,\n",
    "    'JUNIOR_HIGH_SCHOOL' :2,\n",
    "    'ELEMENTARY_SCHOOL'  :1,\n",
    "    'NOT_ATTENDING_SCHOOL':0,\n",
    "    'TECHNICAL_COLLEGE' :4,\n",
    "    'MASTER_DEGREE' : 16,\n",
    "    'DOCTOR_DEGREE' :17,\n",
    "    'BACHELOR_DEGREE' :8,\n",
    "    'OTHERS' : 2  \n",
    "}\n",
    "\n",
    "jobpos_map = {\n",
    "    'Staff' : 1,\n",
    "    'Others': 1,\n",
    "    'Manager': 4,\n",
    "    'Director': 5,\n",
    "    'Supervisor' :2\n",
    "}\n",
    "\n",
    "df['edu_label'] = df['education'].map(ed_map)\n",
    "df['jobpos'] = df['jobpos'].fillna('Staff')\n",
    "df['jobpos_lab'] = df['jobpos'].map(jobpos_map)\n",
    "\n",
    "# \n",
    "df['jobtypeid'] = df['jobtypeid'].fillna('Others')\n",
    "df['jobtypeid_code'] = LE.fit_transform(df.jobtypeid)\n",
    "\n",
    "# large nan value\n",
    "\n",
    "df['EmploymentSinceYear'].replace(0.0, np.random.normal(2005, 7.2), inplace=True) #mode=2010, mean=2005\n",
    "df['EmploymentSinceYear'].fillna(np.random.normal(2005, 7.2), inplace=True) #\n",
    "df['MainBusinessSinceYear'].fillna(np.random.normal(2005, 7.2), inplace=True)\n",
    "# df['avg_income'].fillna(df.avg_income.median(), inplace=True) #np.random.normal(322844761.45, 10106833329)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "id": "2448507f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4182"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.company_lat.value_counts().hist()\n",
    "#df.loc[df['distance_residence_company']==3540124.57188]\n",
    "#df.iloc[212]['MaxOverDueDays']\n",
    "df.std_icnome_area.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "id": "1b8626ef",
   "metadata": {},
   "outputs": [],
   "source": [
    "# drop\n",
    "df.drop('birthdate', axis=1, inplace=True)\n",
    "df.drop('newapplicationdate', axis=1, inplace=True)\n",
    "df.drop('maritalstatus', axis=1, inplace=True)\n",
    "df.drop('education', axis=1, inplace=True)\n",
    "df.drop('professionid', axis=1, inplace=True)\n",
    "df.drop('jobtypeid', axis=1, inplace=True) \n",
    "df.drop('birthplace', axis=1, inplace=True)\n",
    "df.drop('jobpos', axis=1, inplace=True)\n",
    "df.drop('previous', axis=1, inplace=True)\n",
    "df.drop('gender', axis=1, inplace=True)\n",
    "df.drop('MaxOverDueDays', axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "id": "003b3881",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 200000 entries, 0 to 199999\n",
      "Data columns (total 64 columns):\n",
      " #   Column                          Non-Null Count   Dtype  \n",
      "---  ------                          --------------   -----  \n",
      " 0   numofdependence                 200000 non-null  int64  \n",
      " 1   homestatus                      200000 non-null  int64  \n",
      " 2   staysinceyear                   200000 non-null  float64\n",
      " 3   EmploymentSinceYear             200000 non-null  float64\n",
      " 4   MainBusinessSinceYear           200000 non-null  float64\n",
      " 5   monthlyfixedincome              200000 non-null  float64\n",
      " 6   monthlyvariableincome           200000 non-null  float64\n",
      " 7   spouseincome                    199992 non-null  float64\n",
      " 8   residencezipcode                200000 non-null  float64\n",
      " 9   companyzipcode                  195819 non-null  float64\n",
      " 10  legalzipcode                    200000 non-null  float64\n",
      " 11  residence_lat                   200000 non-null  float64\n",
      " 12  residence_long                  200000 non-null  float64\n",
      " 13  company_lat                     195819 non-null  float64\n",
      " 14  company_long                    195819 non-null  float64\n",
      " 15  legal_lat                       200000 non-null  float64\n",
      " 16  legal_long                      200000 non-null  float64\n",
      " 17  avg_income                      195700 non-null  float64\n",
      " 18  std_income                      195700 non-null  float64\n",
      " 19  avg_income_cnt                  195700 non-null  float64\n",
      " 20  avg_income_nation               199882 non-null  float64\n",
      " 21  std_income_nation               199882 non-null  float64\n",
      " 22  avg_income_nation_cnt           199882 non-null  float64\n",
      " 23  avg_income_area                 195818 non-null  float64\n",
      " 24  std_icnome_area                 195818 non-null  float64\n",
      " 25  avg_income_area_cnt             195818 non-null  float64\n",
      " 26  avg_sale_house_price_5000       200000 non-null  float64\n",
      " 27  std_sale_house_price_5000       200000 non-null  float64\n",
      " 28  sale_house_cnt_5000             200000 non-null  float64\n",
      " 29  avg_sale_apartment_price_5000   200000 non-null  float64\n",
      " 30  std_sale_apartment_price_5000   200000 non-null  float64\n",
      " 31  sale_apartment_cnt_5000         200000 non-null  float64\n",
      " 32  avg_rent_house_price_5000       200000 non-null  float64\n",
      " 33  std_rent_house_price_5000       200000 non-null  float64\n",
      " 34  rent_house_cnt_5000             200000 non-null  float64\n",
      " 35  avg_rent_apartment_price_5000   200000 non-null  float64\n",
      " 36  std_rent_apartment_price_5000   200000 non-null  float64\n",
      " 37  rent_apartment_cnt_5000         200000 non-null  float64\n",
      " 38  avg_sale_house_price_10000      200000 non-null  float64\n",
      " 39  std_sale_house_price_10000      200000 non-null  float64\n",
      " 40  sale_house_cnt_10000            200000 non-null  float64\n",
      " 41  avg_sale_apartment_price_10000  200000 non-null  float64\n",
      " 42  std_sale_apartment_price_10000  200000 non-null  float64\n",
      " 43  sale_apartment_cnt_10000        200000 non-null  float64\n",
      " 44  avg_rent_house_price_10000      200000 non-null  float64\n",
      " 45  std_rent_house_price_10000      200000 non-null  float64\n",
      " 46  rent_house_cnt_10000            200000 non-null  float64\n",
      " 47  avg_rent_apartment_price_10000  200000 non-null  float64\n",
      " 48  std_rent_apartment_price_10000  200000 non-null  float64\n",
      " 49  rent_apartment_cnt_10000        200000 non-null  float64\n",
      " 50  distance_residence_company      195819 non-null  float64\n",
      " 51  y                               200000 non-null  int32  \n",
      " 52  age                             200000 non-null  float64\n",
      " 53  newapp_date                     200000 non-null  int64  \n",
      " 54  newapp_month                    200000 non-null  int64  \n",
      " 55  newapp_year                     200000 non-null  float64\n",
      " 56  newapp_day_count                200000 non-null  float64\n",
      " 57  maritalstatus_code              200000 non-null  int32  \n",
      " 58  professionid_code               200000 non-null  int32  \n",
      " 59  birthplace_code                 200000 non-null  int32  \n",
      " 60  gender_code                     200000 non-null  int32  \n",
      " 61  edu_label                       200000 non-null  int64  \n",
      " 62  jobpos_lab                      200000 non-null  int64  \n",
      " 63  jobtypeid_code                  200000 non-null  int32  \n",
      "dtypes: float64(52), int32(6), int64(6)\n",
      "memory usage: 93.1 MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "id": "0a4c999d",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.dropna(how='any',inplace=True) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "id": "aa102d05",
   "metadata": {},
   "outputs": [],
   "source": [
    "# for c in df.columns:\n",
    "#     if df[c].dtype == 'float64' or df[c].dtype == 'int64':\n",
    "#         df[c].plot(kind='box')\n",
    "#         plt.title(c)\n",
    "#         plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "id": "8712f11a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set(style=\"white\", color_codes=True)\n",
    "sns.boxplot(x=\"y\", y=\"avg_income\", data=df )\n",
    "plt.show()\n",
    "\n",
    "sns.boxplot(x=\"y\", y=\"monthlyfixedincome\", data=df )\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "id": "4d5a005e",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\carso\\AppData\\Local\\Programs\\Python\\Python38\\lib\\site-packages\\seaborn\\axisgrid.py:337: UserWarning: The `size` parameter has been renamed to `height`; please update your code.\n",
      "  warnings.warn(msg, UserWarning)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 405.725x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.FacetGrid(df, hue=\"y\", size=5) \\\n",
    "   .map(sns.kdeplot, \"monthlyfixedincome\") \\\n",
    "   .add_legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3996433a",
   "metadata": {},
   "source": [
    "### Build model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "id": "09e861c7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(195692, 52)\n",
      "shape of x_train (136984, 52)\n",
      "shape of x_val (29354, 52)\n",
      "shape of x_train (29354, 52)\n",
      "0    165493\n",
      "1     30199\n",
      "Name: y, dtype: int64\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:ylabel='y'>"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "y = df['y'].copy()\n",
    "df.drop('y', axis=1, inplace=True)\n",
    "\n",
    "#drop some unimprotant features\n",
    "df2 = df.drop('jobpos_lab', axis=1)\n",
    "df2 = df2.drop([\"avg_income_nation\",\"std_income_nation\",'spouseincome',\"avg_income_nation_cnt\"], axis=1)\n",
    "df2 = df2.drop(['newapp_month','jobtypeid_code','newapp_date','gender_code','rent_apartment_cnt_5000',\n",
    "'std_rent_apartment_price_10000'], axis=1)\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(\n",
    "    df2, y, test_size=0.3, random_state=42)\n",
    "\n",
    "X_val, X_test, y_val, y_test = train_test_split(\n",
    "    X_test, y_test, test_size=0.5, random_state=42)\n",
    "\n",
    "print(df2.shape)\n",
    "print('shape of x_train', X_train.shape)\n",
    "print('shape of x_val', X_val.shape)\n",
    "print('shape of x_train', X_test.shape)\n",
    "\n",
    "count = y.value_counts()\n",
    "print(count)\n",
    "sns.barplot(x=count.index, y=count)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "id": "8976fd25",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "roc 0.5019518634200293\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Wall time: 1.79 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "\n",
    "\n",
    "scaler = StandardScaler()\n",
    "X_train_scaled = scaler.fit_transform(X_train)\n",
    "X_test_scaled = scaler.transform(X_test)\n",
    "\n",
    "clf = LogisticRegression(random_state=0, max_iter=1000,C=0.5).fit(X_train_scaled, y_train)\n",
    "\n",
    "#clf = SVC(gamma='auto',C=0.8, max_iter=300,probability=True) #'poly'\n",
    "#clf.fit(X_train_scaled, y_train)\n",
    "\n",
    "pred = clf.predict(X_test.values)\n",
    "y_pred_proba = clf.predict_proba(X_test.values)[:,1]\n",
    "fpr, tpr, _ = roc_curve(y_test,  y_pred_proba)\n",
    "print('roc',roc_auc_score(pred, y_test))\n",
    "#no skill model -0.5\n",
    "ns_probs = [0 for _ in range(y_test.shape[0])]\n",
    "ns_fpr, ns_tpr, _ = roc_curve(y_test, ns_probs)\n",
    "\n",
    "\n",
    "#create ROC curve\n",
    "plt.plot(fpr,tpr, label='Model')\n",
    "plt.plot(ns_fpr, ns_tpr, linestyle='--', label='No Skill')\n",
    "plt.ylabel('True Positive Rate')\n",
    "plt.xlabel('False Positive Rate')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "07c6be49",
   "metadata": {},
   "source": [
    "### LGBM tree"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ebd14c91",
   "metadata": {},
   "outputs": [],
   "source": [
    "clf = lgb.LGBMClassifier()\n",
    "clf.fit(X_train, y_train)\n",
    "pred=clf.predict(X_test)\n",
    "\n",
    "print('f1', f1_score(pred, y_test))\n",
    "print('auc score: ',roc_auc_score(y_pred, y_test))\n",
    "\n",
    "y_pred_proba = clf.predict_proba(X_test.values)[:,1]\n",
    "fpr, tpr, _ = roc_curve(y_test,  y_pred_proba)\n",
    "\n",
    "#no skill model -0.5\n",
    "ns_probs = [0 for _ in range(y_test.shape[0])]\n",
    "ns_fpr, ns_tpr, _ = roc_curve(y_test, ns_probs)\n",
    "\n",
    "\n",
    "#create ROC curve\n",
    "plt.plot(fpr,tpr, label='Model')\n",
    "plt.plot(ns_fpr, ns_tpr, linestyle='--', label='No Skill')\n",
    "plt.ylabel('True Positive Rate')\n",
    "plt.xlabel('False Positive Rate')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "id": "2ca81be5",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\carso\\AppData\\Local\\Programs\\Python\\Python38\\lib\\site-packages\\lightgbm\\sklearn.py:736: UserWarning: 'verbose' argument is deprecated and will be removed in a future release of LightGBM. Pass 'log_evaluation()' callback via 'callbacks' argument instead.\n",
      "  _log_warning(\"'verbose' argument is deprecated and will be removed in a future release of LightGBM. \"\n",
      "C:\\Users\\carso\\AppData\\Local\\Programs\\Python\\Python38\\lib\\site-packages\\lightgbm\\engine.py:177: UserWarning: Found `num_iterations` in params. Will use it instead of argument\n",
      "  _log_warning(f\"Found `{alias}` in params. Will use it instead of argument\")\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[LightGBM] [Warning] min_data_in_leaf is set=25, min_child_samples=20 will be ignored. Current value: min_data_in_leaf=25\n",
      "[LightGBM] [Warning] lambda_l1 is set=0.005, reg_alpha=0.0 will be ignored. Current value: lambda_l1=0.005\n",
      "[LightGBM] [Warning] bagging_fraction is set=0.8, subsample=1.0 will be ignored. Current value: bagging_fraction=0.8\n",
      "auc score:  0.8196634030227705\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Wall time: 2min 41s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "#building model\n",
    "Booster = lgb.LGBMClassifier(learning_rate=0.01,\n",
    "                        num_iterations=2000,\n",
    "                        num_leaves=20, #d 31 =2^max_dep不能太大 20~0.83\n",
    "                        max_depth=10,  #\n",
    "                        min_data_in_leaf=25, #default=20\n",
    "                        max_bin=255, #default=255\n",
    "                        lambda_l1=0.005,\n",
    "                        boosting_type='dart',\n",
    "                        drop_rate=0.2,\n",
    "                        objective='cross_entropy',\n",
    "                        bagging_fraction=0.8,\n",
    "                        tree_learner='voting',\n",
    "                        seed=10\n",
    "                        )\n",
    "\n",
    "Booster.fit(X_train, y_train,\n",
    "       eval_set=[(X_val, y_val), (X_train, y_train)], verbose=-1, eval_metric='auc')\n",
    "pred = Booster.predict(X_test)\n",
    "\n",
    "\n",
    "print('auc score: ',roc_auc_score(pred, y_test))\n",
    "\n",
    "y_pred_proba = Booster.predict_proba(X_test.values)[:,1]\n",
    "fpr, tpr, _ = roc_curve(y_test,  y_pred_proba)\n",
    "\n",
    "#no skill model -0.5\n",
    "ns_probs = [0 for _ in range(y_test.shape[0])]\n",
    "ns_fpr, ns_tpr, _ = roc_curve(y_test, ns_probs)\n",
    "\n",
    "\n",
    "#create ROC curve\n",
    "plt.plot(fpr,tpr, label='Model')\n",
    "plt.plot(ns_fpr, ns_tpr, linestyle='--', label='No Skill')\n",
    "plt.ylabel('True Positive Rate')\n",
    "plt.xlabel('False Positive Rate')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "id": "c4d97503",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Plotting metrics recorded during training...\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print('Plotting metrics recorded during training...')\n",
    "ax = lgb.plot_metric(Booster,metric='auc')\n",
    "ax2 = lgb.plot_metric(Booster,metric='cross_entropy')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "id": "382cc6e6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "lgb.plot_importance(Booster, max_num_features=20, height=2, figsize=(10,10))\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "id": "057e823b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.metrics import confusion_matrix,accuracy_score\n",
    "import seaborn as sns\n",
    "\n",
    "cfm = confusion_matrix(y_test, pred)\n",
    "acc = accuracy_score(y_test, pred)\n",
    "sns.heatmap(cfm, annot=True, fmt=\".0f\", linewidths=.5, square = True, cmap = 'Blues_r');\n",
    "plt.ylabel('Actual label');\n",
    "plt.xlabel('Predicted label');\n",
    "all_sample_title = 'Accuracy Score: {0}'.format(acc)\n",
    "plt.title(all_sample_title, size = 15);"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "89d285d6",
   "metadata": {},
   "source": [
    "## 深度学习方法\n",
    "\n",
    "没有达到预期效果\n",
    "可能性：数据不平衡？归一化？"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5067aa0b",
   "metadata": {},
   "outputs": [],
   "source": [
    "df2.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ceec5e84",
   "metadata": {},
   "outputs": [],
   "source": [
    "import tensorflow as tf\n",
    "from tensorflow import keras\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "# X_train, X_test, y_train, y_test = train_test_split(\n",
    "#     df2, y, test_size=0.3, random_state=42)\n",
    "#target = y\n",
    "\n",
    "#scaler = StandardScaler()\n",
    "scaler = MinMaxScaler()\n",
    "X_train_scaled = scaler.fit_transform(X_train)\n",
    "X_test_scaled = scaler.transform(X_test)\n",
    "\n",
    "dataset_train = tf.data.Dataset.from_tensor_slices((X_train_scaled, y_train.values))\n",
    "dataset_test = tf.data.Dataset.from_tensor_slices((X_test_scaled, y_test.values))\n",
    "\n",
    "dataset_train_batch = dataset_train.shuffle(len(dataset_train)).batch(256)\n",
    "dataset_test_batch = dataset_test.shuffle(len(dataset_test)).batch(256)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8b8de2c9",
   "metadata": {},
   "outputs": [],
   "source": [
    "tf.random.set_seed(42)\n",
    "np.random.seed(42)\n",
    "from sklearn.metrics import roc_auc_score, f1_score\n",
    "\n",
    "def auroc(y_true, y_pred):\n",
    "    return f1_score(y_true.numpy(), y_pred.numpy())\n",
    "\n",
    "\n",
    "def get_compiled_model():\n",
    "    model = tf.keras.Sequential([\n",
    "    tf.keras.layers.Dense(58, activation='relu', kernel_initializer=\"he_normal\",\n",
    "                         kernel_regularizer=keras.regularizers.l2(0.01)),\n",
    "    keras.layers.BatchNormalization(),\n",
    "    tf.keras.layers.Dense(256, activation='relu', kernel_initializer=\"he_normal\",\n",
    "                         kernel_regularizer=keras.regularizers.l2(0.01)),\n",
    "    keras.layers.BatchNormalization(),\n",
    "    keras.layers.Dropout(rate=0.2),\n",
    "    tf.keras.layers.Dense(300, activation='relu', kernel_initializer=\"he_normal\",\n",
    "                         kernel_regularizer=keras.regularizers.l2(0.01)),\n",
    "    keras.layers.BatchNormalization(),\n",
    "     keras.layers.Dropout(rate=0.3),\n",
    "    tf.keras.layers.Dense(300, activation='relu', kernel_initializer=\"he_normal\",\n",
    "                         kernel_regularizer=keras.regularizers.l2(0.01)),\n",
    "    tf.keras.layers.Dense(1, activation=\"sigmoid\"), #\"softmax\"\n",
    "  ])\n",
    "    \n",
    "    optimizer = keras.optimizers.Adam(learning_rate=0.003)\n",
    "    model.compile(optimizer=optimizer,\n",
    "                loss='binary_crossentropy',\n",
    "                metrics=['accuracy'])\n",
    "    \n",
    "    return model\n",
    "\n",
    "model = get_compiled_model()\n",
    "history = model.fit(dataset_train_batch,  validation_data=dataset_test_batch,epochs=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e75de714",
   "metadata": {},
   "outputs": [],
   "source": [
    "# summarize history for loss\n",
    "plt.plot(history.history['loss'])\n",
    "plt.plot(history.history['val_loss'])\n",
    "plt.title('model loss')\n",
    "plt.ylabel('loss')\n",
    "plt.xlabel('epoch')\n",
    "plt.legend(['train', 'test'], loc='upper left')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "21dd7c3d",
   "metadata": {},
   "source": [
    "### 过采样添加label"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a4315a71",
   "metadata": {},
   "outputs": [],
   "source": [
    "# df2.shape\n",
    "# idx = np.arange(30000)\n",
    "# np.random.shuffle(arr)\n",
    "# df3= df2.iloc[idx]\n",
    "# y3 = y.iloc[idx]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2082e33c",
   "metadata": {},
   "outputs": [],
   "source": [
    "# # decision tree  on imbalanced dataset with SMOTE oversampling and random undersampling\n",
    "# from numpy import mean\n",
    "# import imblearn\n",
    "\n",
    "# from sklearn.model_selection import cross_val_score\n",
    "# from sklearn.model_selection import RepeatedStratifiedKFold\n",
    "# from sklearn.ensemble import RandomForestClassifier\n",
    "# from imblearn.pipeline import Pipeline\n",
    "# from imblearn.over_sampling import SMOTE\n",
    "# from imblearn.under_sampling import RandomUnderSampler\n",
    "\n",
    "# # define pipeline\n",
    "# from imblearn.over_sampling import ADASYN\n",
    "# from collections import Counter\n",
    "\n",
    "# oversample = ADASYN()\n",
    "\n",
    "# X_train, X_test, y_train, y_test = train_test_split(\n",
    "#     df2, y, test_size=0.3, random_state=42)\n",
    "\n",
    "# #X_train, y_train = oversample.fit_resample(X_train.values, y_train)\n",
    "\n",
    "\n",
    "# model = RandomForestClassifier()\n",
    "# model.fit(X_train, y_train)\n",
    "# pred = model.predict(X_test)\n",
    "\n",
    "# print('auc score: ',roc_auc_score(pred, y_test))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1b7c15f6",
   "metadata": {},
   "source": []
  },
  {
   "attachments": {
    "image.png": {
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"
    }
   },
   "cell_type": "markdown",
   "id": "fa72d486",
   "metadata": {},
   "source": [
    "### 2. Write code to illustrate statistical concepts:\n",
    "Explain the following statistical concepts and write some code to illustrate how they work:\n",
    "\n",
    "- Regularization: 正则化， 为了防止过拟合在目标函数中加入正则项，使得模型参数更小（l2)或者等于零（l1)相对于降低模型复杂度。\n",
    "\n",
    "\n",
    "\n",
    "- Overfitting\n",
    "过拟合，模型过于复杂，学习到训练数据中泛化能力低的部分，甚至是outliers, 以至于training loss在不断降低而testing loss和test acc在降低。\n",
    "\n",
    "如图，之前的例子，训练的迭代次数很大，train loss和valid loss开始向反方向走了。\n",
    "![image.png](attachment:image.png)\n",
    "\n",
    "\n",
    "- Ensemble\n",
    "\n",
    "之前的LighBGM例子中， 是将决策树作为基学习器的集成算法。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "id": "d99359b5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "shape of x_train (353, 10)\n",
      "No Regularization -3036.7249855614987\n",
      "Regularization=1 -3826.7739038244417\n",
      "Regularization=10 -5943.716736095004\n"
     ]
    }
   ],
   "source": [
    "from sklearn.datasets import load_diabetes\n",
    "\n",
    "load = load_diabetes()\n",
    "df_dia = pd.DataFrame(load.data, columns=load.feature_names)\n",
    "y_dia = pd.Series(load.target)\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(\n",
    "    df_dia , y_dia, test_size=0.2, random_state=42)\n",
    "print('shape of x_train', X_train.shape)\n",
    "\n",
    "#Regularization\n",
    "# R2\n",
    "from sklearn.linear_model import Ridge\n",
    "from sklearn import datasets, linear_model\n",
    "from sklearn.model_selection import cross_val_score\n",
    "diabetes = datasets.load_diabetes()\n",
    "X = diabetes.data\n",
    "y = diabetes.target\n",
    "lasso = linear_model.Lasso(alpha=0.1)\n",
    "print('No Regularization', cross_val_score(lasso, X, y, cv=3, scoring='neg_mean_squared_error').mean())\n",
    "\n",
    "lasso = linear_model.Lasso(alpha=1)\n",
    "print('Regularization=1', cross_val_score(lasso, X, y, cv=3, scoring='neg_mean_squared_error').mean())\n",
    "\n",
    "lasso = linear_model.Lasso(alpha=5)\n",
    "print('Regularization=10', cross_val_score(lasso, X, y, cv=3, scoring='neg_mean_squared_error').mean())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "id": "361ecc96",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "alphas = np.linspace(0.01,50,100)\n",
    "lasso = linear_model.Lasso(max_iter=1000)\n",
    "coefs = []\n",
    "\n",
    "for a in alphas:\n",
    "    lasso.set_params(alpha=a)\n",
    "    lasso.fit(X_train, y_train)\n",
    "    coefs.append(lasso.coef_)\n",
    "\n",
    "ax = plt.gca()\n",
    "\n",
    "ax.plot(alphas, coefs)\n",
    "ax.set_xscale('log')\n",
    "plt.axis('tight')\n",
    "plt.xlabel('alpha')\n",
    "plt.ylabel('Standardized Coefficients')\n",
    "plt.title('Lasso coefficients as a function of alpha');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "85279ef4",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
